# Rent a dedicated NVIDIA L40S for $890 per month

> L40S rental: a dedicated NVIDIA L40S 48 GB for $890/month, about $1.22 per GPU-hour at 24/7, for inference, image generation and rendering. Paid in crypto.

Source: https://overgpu.com/gpus/nvidia-l40s  
Updated: 2026-10-23  
Publisher: OverGPU (https://overgpu.com/)

**In short:** OverGPU rents the NVIDIA L40S by the month: a dedicated single L40S (48 GB GDDR6) for $890 per month, about $1.22 per GPU-hour at full use. It is in stock and delivered in under 10 minutes after payment. Payment is from a USD balance topped up in Bitcoin, Ethereum, Monero, USDT (TRC-20) or Solana, with no setup fee, and the plan can be cancelled before any monthly renewal.

## L40S configurations and monthly prices at OverGPU

| Configuration | Type | GPU memory | CPU | RAM | Storage | Network | Price per month (USD) | Per GPU-hour | Availability |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| [NVIDIA L40S](https://overgpu.com/gpus/nvidia-l40s) | Single GPU | 48 GB GDDR6 | 16 vCPU | 128 GB | 1 TB NVMe | 10 Gbps | $890 | $1.22 | In stock, delivered in under 10 minutes after payment |

Per GPU-hour = monthly price ÷ number of GPUs ÷ 730 hours, shown only to compare with hourly offers: every plan is billed as one flat monthly price.

## NVIDIA L40S specifications (vendor-published)

| Specification | Value | Note |
| --- | --- | --- |
| Architecture | Ada Lovelace | NVIDIA |
| GPU memory | 48 GB GDDR6 ECC |   |
| Memory bandwidth | 864 GB/s |   |
| FP4 | — | Not supported |
| FP8 | 733 TFLOPS | 1,466 TFLOPS with sparsity |
| BF16 / FP16 | 362 TFLOPS | 733 TFLOPS with sparsity |
| GPU interconnect | PCIe Gen4 x16 | no NVLink |
| Form factor | Dual-slot PCIe card |   |
| Max power | 350 W |   |

## What the L40S is best at

- **Inference of 7B to 14B models:** An 8B model in BF16 uses about 16 GB, leaving most of the 48 GB for KV cache.
- **Image generation:** SDXL and 12B-parameter image models such as FLUX.1 run in BF16 with room for batches.
- **Rendering and media:** Ada RT cores and NVENC video encoders for ray-traced rendering and transcoding.
- **LoRA and QLoRA:** LoRA on 8B models and QLoRA on models up to about 32B fit in 48 GB.

## When another GPU fits better

- [Rent RTX PRO 6000 Blackwell servers from $1,390 per month](https://overgpu.com/gpus/nvidia-rtx-pro-6000): When you need 96 GB per GPU, FP4 or an 8-GPU server, from $1,390 per month.
- [Rent NVIDIA H100 SXM servers from $2,190 per month](https://overgpu.com/gpus/nvidia-h100): For QLoRA on 70B models, high-concurrency serving or training, where 80 GB of HBM3 at 3.35 TB/s matters.
- [Rent a dedicated RTX 5090 cloud server for $449 per month](https://overgpu.com/gpus/nvidia-rtx-5090): For prototyping and image generation that fit in 32 GB, at $449 per month.

## Overview

When you rent NVIDIA L40S capacity from us, you get one whole Ada Lovelace GPU with 48 GB of GDDR6 in a dedicated virtual machine, for $890 per month. It is our entry-level data-center GPU: large enough for 7B–14B language models in BF16, image generation and GPU rendering, and it is in stock now.

## What the L40S is, and where it fits in 2026

The L40S is NVIDIA's Ada Lovelace data-center GPU. Per NVIDIA's specifications, it has 48 GB of GDDR6 at 864 GB/s and Tensor Cores with FP8 support at 733 TFLOPS dense (1,466 TFLOPS with sparsity), plus 362 TFLOPS of dense BF16. It is a 350 W PCIe Gen4 card without NVLink. Unlike compute-only GPUs such as the H100, it also carries RT cores and NVENC video encoders, so the same card can serve models, render scenes and transcode video.

In 2026 it sits at the value end of our data-center lineup. The H100 has about 3.9 times its memory bandwidth and NVIDIA lists 2.7 times its dense FP8 compute, which matters for large models and high concurrency. The L40S makes sense when your model fits in 48 GB and the monthly bill matters more than peak throughput. Below it in price, the GeForce RTX 5090 has more bandwidth (1.79 TB/s) but only 32 GB; the L40S's extra 16 GB is what lets a 14B model run in BF16 with a useful KV cache, or an image pipeline keep its text encoders resident. Our [L40S vs H100](https://overgpu.com/compare/l40s-vs-h100) and [RTX PRO 6000 vs L40S](https://overgpu.com/compare/rtx-pro-6000-vs-l40s) comparisons cover both directions.

## What fits in 48 GB

Weights take roughly 2 bytes per parameter in BF16, 1 in FP8 and 0.5 in 4-bit, plus KV cache and 10–30% headroom.

- **7B–8B models in BF16:** about 16 GB of weights, leaving roughly 25 GB for KV cache after headroom: comfortable for chat, RAG and agent backends.
- **14B models in BF16:** about 28 GB, with around 15 GB left for KV cache. In FP8, the same model takes about 14 GB.
- **32B models:** about 32 GB in FP8, which works for short contexts and modest concurrency; in 4-bit, at about 16 GB, there is plenty of room.
- **70B models:** FP8, at about 70 GB, does not fit. 4-bit, at about 35 GB, does, but leaves too little KV cache for production use. Move to a 96 GB RTX PRO 6000, or to an H200 for more KV cache, instead.
- **Image generation:** SDXL, with about 3.5B parameters, runs with large batches, and a 12B-parameter model such as FLUX.1, about 24 GB in BF16, fits together with its text encoders.

For training, LoRA on an 8B model in BF16 and QLoRA on models up to about 32B fit comfortably. QLoRA on 70B is possible but tight. Full fine-tuning with Adam needs roughly 16–20 bytes per parameter, so on 48 GB it is limited to small models of around 1–2B parameters. Our article on [how much VRAM LLMs need](https://overgpu.com/blog/vram-for-llms) explains the KV cache side of the math.

## Rendering, video and graphics workloads

The L40S's RT cores accelerate GPU renderers such as Blender Cycles with OptiX, Redshift and Octane. The scene has to fit in the GPU's 48 GB, or the renderer falls back to slower out-of-core modes where it supports them; 48 GB is enough for many look-dev and mid-size scenes. The NVENC encoders handle video transcoding next to AI work, for example encoding the output of a generation pipeline on the same machine. If your scenes outgrow 48 GB, the RTX PRO 6000 doubles the memory. See [rendering and VFX](https://overgpu.com/solutions/rendering-vfx) for more on render workloads.

## Software on an L40S server

The VM comes with root access over SSH and Ubuntu 22.04 or 24.04 with NVIDIA drivers, CUDA, Docker and the NVIDIA Container Toolkit preinstalled; Debian or Rocky Linux are available on request. Ada's Tensor Cores support FP8, and both vLLM and TensorRT-LLM use it on this GPU, so FP8 checkpoints run on the hardware FP8 path. A typical small-model endpoint:

```
vllm serve <your-8b-model> --quantization fp8 --max-model-len 32768 \
  --host 127.0.0.1 --port 8000
```

Binding to localhost keeps the endpoint private until you put your own authentication in front of it. Our guide to [self-hosting inference with vLLM](https://overgpu.com/blog/self-host-llm-inference-vllm) goes further.

## L40S price per month and per GPU-hour

The L40S costs $890 per month, about $1.22 per GPU-hour if it runs all 730 hours. For an always-on endpoint, a render queue or a batch pipeline that never empties, that flat price is the whole bill. For occasional jobs it is not: at 100 hours of use a month, the effective cost is $8.90 per used hour, and hourly billing elsewhere will usually cost less. The break-even point is simply $890 divided by the hourly rate you would pay.

One sizing trap to avoid: two L40S plans cost $1,780 per month for 96 GB split across two GPUs, while one RTX PRO 6000 costs $1,390 for 96 GB on a single card. If your model needs more than 48 GB, the larger card is both simpler and less expensive, because nothing has to be split across GPUs.

The price includes 20 TB of outbound traffic per month with no egress fees within that allowance, a public IPv4 address and an IPv6 /64, DDoS mitigation, 24/7 support from engineers and the 99.9% availability SLA. There is no setup fee. The L40S is offered as a single-GPU plan only; if you rent several, [private networking](https://overgpu.com/products/private-networking) between your servers is available on request.

## Ordering an L40S

[Deploy it](https://overgpu.com/deploy) in one form: choose the plan, set up the OS image, optional preinstalled software and your login (root password or SSH key), create a free account or log in, and pay the first month from your prepaid balance, which you top up in BTC, ETH, XMR, USDT on TRON or SOL (card payments are coming soon). The server is delivered in under 10 minutes after payment, with the IP and SSH details in your dashboard. You can cancel any month with no fee, the server running until the end of the paid month. For sizing before you order, use the [VRAM calculator](https://overgpu.com/tools/vram-calculator).

## Frequently asked questions

### How much does it cost to rent an NVIDIA L40S?

The L40S is $890 per month, about $1.22 per GPU-hour if it runs all month. The price includes 20 TB of outbound traffic, a public IPv4 address, DDoS mitigation and 24/7 support, with no setup fee.

### Can the L40S run a 70B model?

Only in 4-bit, where the weights take about 35 GB and leave little room for KV cache. For 70B in FP8, use an RTX PRO 6000 with 96 GB, or an H200 with 141 GB for more KV cache.

### Does the L40S support FP8?

Yes. Its Ada Lovelace Tensor Cores support FP8, which NVIDIA lists at 733 TFLOPS dense and 1,466 TFLOPS with sparsity.

### Can I get an 8-GPU L40S server?

We offer the L40S as a single-GPU plan only. For eight GPUs in one server, the RTX PRO 6000 server is the closest option, and several single plans can be linked with private networking on request.

### Is the L40S shared or partitioned?

No. You get the whole GPU, passed through to a dedicated virtual machine, with no vGPU slicing, MIG partitions or time-sharing.

### How fast can I get an L40S?

It is in stock at the time of writing, and it is delivered in under 10 minutes after payment.

## Related pages

- [Single GPU server rental: a whole GPU in a dedicated VM](https://overgpu.com/products/single-gpu-servers): One whole GPU in a dedicated cloud VM, from RTX 5090 to B300. Root access, 20 TB bandwidth, monthly billing, delivered in under 10 minutes.
- [GPU servers for LLM inference, running 24/7 at a flat price](https://overgpu.com/solutions/inference): KV cache sizing, FP8 and FP4, and 20–50 TB of bandwidth included. See the sizing math.
- [A dedicated GPU for image and video generation](https://overgpu.com/solutions/generative-media): Approximate VRAM for Stable Diffusion, Flux and video models, on dedicated GPUs. Compare the cards.
- [GPU cloud for AI startups, with predictable monthly spend](https://overgpu.com/solutions/startups): Dedicated H100, H200 and Blackwell GPUs at a flat monthly price, paid in crypto, cancel any month. See how to size and budget.
- [LLM VRAM calculator: will your model fit on a GPU?](https://overgpu.com/tools/vram-calculator): Pick Llama, Qwen, Mistral or gpt-oss, set the precision and context length, and see weights, KV cache and which GPUs fit.
- [Best GPU for LLM inference in 2026: a sizing guide by model size](https://overgpu.com/blog/best-gpu-for-llm-inference): Use our 2026 sizing table, from RTX 5090 to 8× B300, to choose.